[Distutils] Status update on the NumPy & SciPy vs SSE problem?

David Cournapeau cournape at gmail.com
Fri Feb 5 04:33:15 EST 2016

On Thu, Feb 4, 2016 at 11:42 AM, Antoine Pitrou <solipsis at pitrou.net> wrote:

> On Thu, 4 Feb 2016 21:22:32 +1000
> Nick Coghlan <ncoghlan at gmail.com> wrote:
> >
> > I figured that was independent of the manylinux PEP (since it affects
> > Windows as well), but I'm also curious as to the current status (I
> > found a couple of apparently relevant threads on the NumPy list, but
> > figured it made more sense to just ask for an update rather than
> > trusting my Google-fu)
> While I'm not a Numpy maintainer, I don't think you can go much further
> than SSE2 (which is standard under the x86-64 definition).
> One factor is support by the kernel. The CentOS 5 kernel doesn't
> seem to support AVX, so you can't use AVX there even if your processor
> supports it (as the registers aren't preserved accross context
> switches). And one design point of manylinux is to support old Linux
> setups... (*)

I don't have precise numbers, but I can confirm we get from times to times
some customer reports related to avx not being supported (because of CPU or

> There are intermediate ISA additions between SSE2 and AVX (additions
> that don't require OS support), but I'm not sure they help much on
> compiler-vectorized code as opposed to hand-written assembly.  Numpy's
> pre-compiled loops are typically quite straightforward as far as I've
> seen.
> One mitigation is to delegate some operations to an optimized library
> implementing the appropriate runtime switches: for example linear
> algebra is delegated by Numpy and Scipy to optimized BLAS and LINPACK
> libraries (which exist in various implementations such as OpenBLAS or
> Intel's MKL).
> (*) (this is an issue a JIT compiler helps circumvent: it generates
> optimal code for the current CPU ;-))
> Regards
> Antoine.
> _______________________________________________
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